Factor the data into categorical variables
# Refactoring Columns for samples
data2$Sample_ID <- as.factor(data2$Sample_ID)
data2$Dilution_factor <- as.numeric(data2$Dilution_factor)
data2$Injection<- as.factor(data2$Injection)
data2$Tech_rep <- as.numeric(data2$Tech_rep)
# Refactoring COlumns for key
key$Sample_ID <- as.factor(key$Sample_ID)
key$Time <- as.factor(key$Time)
key$Treatment <- as.factor(key$Treatment)
key$Volume <- as.numeric(key$Volume)
key$Patient_ID <- as.factor(key$Patient_ID)
key$Treatment <- factor(key$Treatment,levels = c('DMSO','EGF','BPS','BPS_EGF'))
key$Patient_ID <- factor(key$Patient_ID,levels = c('1','5','7'))
# Refactoring columns for standards
standards2$Sample_ID <- as.factor(standards2$Sample_ID)
standards2$When <- as.factor(standards2$When)
standards2$Dilution_factor <- as.numeric(standards2$Dilution_factor)
standards2$Injection <- as.factor(standards2$Injection)
standards2$Nano_day <- as.numeric(standards2$Nano_day)
Back calculate standards
standards2 <- standards2 %>%
mutate(True_Count=Dilution_factor*Count)
# Set the correct order of 'categorical factors'
standards2$Nano_day <- factor(standards2$Nano_day, levels=c('1','2','3','4'))
standards2$When <- factor(standards2$When, levels=c('before','after'))
Summarize three technical standard replicates
standards3 <- standards2 %>%
group_by(particle_size,Sample_ID,When,Dilution_factor,Nano_day,Injection) %>%
summarise( tech_N = length(True_Count),
tech_mean = mean(True_Count),
tech_sd = sd(True_Count),
tech_se = tech_sd/sqrt(tech_N))
standards3
Summarize standards by injection
standards4 <- standards3 %>%
group_by(Nano_day,When,particle_size) %>%
summarise( inj_N = length(tech_mean),
inj_mean = mean(tech_mean),
inj_sd = sd(tech_mean),
inj_se = inj_sd/sqrt(inj_N))
standards4
Plot before and after plots, facet by experimental day
std_plot <- standards4 %>%
ggplot(aes(x = particle_size, y = inj_mean, color=When))+
geom_line(size=2) + xlim(0,300)+ #line size, x-axis scale
geom_ribbon(aes(ymin=inj_mean-inj_se, ymax=inj_mean+inj_se),
alpha=0.4,fill = alpha('grey12', 0.2)) + #error bars
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Particle Size") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\n100nm Standards")+ #title
labs(color="Condition")+ #Label table title
facet_wrap(~ Nano_day)
std_plot
## Warning: Removed 1400 rows containing missing values (geom_path).

# ggsave("Standards_histogram_plot.png",
# height = 5, width = 7, dpi = 300, units= "in")
Standards particle concentrations from each experimental day
standards_df <- standards4 %>%
group_by(Nano_day,When) %>%
summarise(total=sum(inj_mean))
standards_df
Bar graph of standards particle concentrations
standards_bar <- standards_df %>%
ggplot(aes(x=Nano_day,y=total,fill=When))+
geom_col(position="dodge")+
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Experimental Day") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\n100nm Standards")+ #title
labs(color="When") #Label table title
standards_bar

# ggsave("Standards_bar_plot.png",
# height = 5, width = 7, dpi = 300, units= "in")
Intraassay variability
Intra.assay_cv <- standards_df %>%
group_by(Nano_day) %>%
summarise(Intra_Day_N = length(total),
Intra_Day_mean = mean(total),
Intra_Day_sd = sd(total),
Intra_Day_se = Intra_Day_sd/sqrt(Intra_Day_N),
Intra_Day_cv = Intra_Day_sd/Intra_Day_mean )
Intra.assay_cv
# # Save as .csv
# write_csv(Intra.assay_cv,"Intra.assay_cv.csv")
Interassay variability
Inter.assay_cv <- Intra.assay_cv %>%
summarise(Inter_Day_N = length(Intra_Day_mean),
Inter_Day_mean = mean(Intra_Day_mean),
Inter_Day_sd = sd(Intra_Day_mean),
Inter_Day_se = Inter_Day_sd/sqrt(Inter_Day_N),
Inter_Day_cv = Inter_Day_sd/Inter_Day_mean )
Inter.assay_cv
# # Save as .csv
# write_csv(Inter.assay_cv,"Inter.assay_cv.csv")